粒子群优化算法的发展趋势  

The developing trend of particle swarm optimization algorithm

作　　者：莫愿斌[1] 刘贺同[1] 陈德钊[2] 

机构地区：[1]广西民族大学,数学与计算机科学学院,广西南宁530006 [2]浙江大学,智能信息工程研究所,浙江杭州310027

出　　处：《计算机与应用化学》2009年第4期430-434,共5页Computers and Applied Chemistry

基　　金：广西民族大学引进人才科研启动项目(200702YJ14)

摘　　要：分析优化算法的发展历程,指出粒子群优化算法(PSO)是基于群体智能的一种算法,简单易行,可调参数少,研究广泛且发展迅速。结合图形给出算法的个体极值和整体极值的搜优运动过程。研究总结算法的研究现状及特点,认为PSO还需要完善和继续研究。提出将算法应用于复杂的约束优化、随机优化与最优控制问题是算法应用研究的方向,并指出对该算法完整的收敛性分析是算法成熟的标志。The developing course of optimization algorithm was analyzed, and the results point out that Particle Swarm Optimization （PSO） algorithm is an algorithm based on swarm intelligence, simple to operate, less adjustable parameters, widely studied and rapidly developed. The seeking procedure of the personal best and the global best in PSO was given by figure. After analyzing the research state and characteristics of PSO, further perfection and continual study were considered. It was proposed that solving complex constrained optimization problems, stochastic optimization problems, optimal control problems are PSO＇ applying study proposed and perfect convergent analyzing for PSO is its mature sign.

关 键 词：优化 粒子群优化算法 发展趋势 收敛性 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TQ015.9[自动化与计算机技术—控制科学与工程]